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Earhquake is one of the most hazardous, devasting natural calamity and yet a very least predictable natural disaster that occur. Prediction of earthquake has been a challenging research for many researchers. With the increasing amount of earthquake dataset collected, many researchers try to solve the task of predicting the earthquake in future time. Even though many data mining techniques are been used, the prediction rate is not still accurate due to lack of feature extraction technique. The proposed methodology enhance the performance of earthquake prediction. As obtained precursory pattern features along with Random forest regression is used to get prediction of the magnitude of future earthquakes.

Blockchain is considered a shared ledger technology to be a viable alternative for heavily focused control process applications. However, most of the current blockchain networks are used for the proof of work called the Hash-Puzzle-Solving Consensus Protocol. The competition to solve the puzzle leads to high latency, leading directly to a lengthy cycle of transaction processing. One solution to this problem is the establishment of a shards blockchain network. In this article, we concentrate on the blockchain network and follow a consensus protocol on security deposits to explore the issue of how to reconcile the economic reward and incentive. The knowledge asymmetric dilemma between beacon and users is also posed by the intrinsic features of the blockchain, i.e. transparency and decentralization. The philosophy of the contract is used to describe the dilemma. The optimum incentives for the various types of validators as well as the required deposits will also be obtained. The flexible deposits may provide ample economic stimulus for participants relative to fixed deposits without sacrificing the safety incentives. Moreover, the results of simulation suggest that the contract theory strategy will optimize the effectiveness of the beacon chain and fulfil the participants’ reward compatibility and individual rationality.

Internet of Things (IoT) is an emerging research field in networking domain and applied to almost all the applications that can change the people lives as smart. Moreover, in some use cases large volume of sensitive data could be generated. The number of security threats related to Infrastructure, platform and application of IoT has been increased over the last few years. So, it is necessary to apply proper security solutions which ensure privacy and confidentiality of data. To address the secure and reliable communication, various trust-based solutions were introduced. Most of the nodes in IoT system are heterogeneous and limited storage space. The existing trust management protocols do not scale well to accommodate this requirement. This article provides a detailed review of the security challenges and trust management techniques adopted for IoT to secure data in a cloud environment.

The internet of things (IoT) is a technological revolution that has recently become more important to the real world because of the growth of smart devices, embedded and ubiquitous communication technologies and combined with cyber world to provide many smart services. These services are particularly creating new challenges in security and privacy concerns. To address this issue, trust management system and access control system must be focused in IoT. This paper presents a trust aware access control system using fuzzy logic (TAACS-FL) for IoT. Access control is an important mechanism to ensure only trusted users/devices access the data from the sensor device or command the actuators to perform some task in the IoT context. First, we monitor the devices and gather the trust parameters like successful forward ratio (SFR), data integrity (DI) and energy consumption rate (ECR). Second, by using fuzzy engine trust parameters are combined and overall trust value is calculated. Third, based on the trust value access control method is defined. NS-2 based simulation result shows TAACS-FL guarantees scalability and energy efficient.